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Performance of an artificial intelligence model for evaluation of unnecessary central lines, Northern California 2025.
Performance of an artificial intelligence model for evaluation of unnecessary central lines, Northern California 2025. Infection control and hospital epidemiology Wick, J. M., Bhaskara, A., Tariq, W., Lau, S., Sampson, M. M., Salinas, J. L. 2026: 1-3Abstract
We used a large language model integrated in the electronic health record to evaluate unnecessary central lines. It had a 16% sensitivity and 99% specificity for detecting unnecessary lines. Although it missed many unnecessary lines, the high specificity suggests potential as a tool where human review is not feasible.
View details for DOI 10.1017/ice.2026.10462
View details for PubMedID 42037194